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2021 | OriginalPaper | Chapter

Retinopathy Detection Using Probabilistic Neural Network

Authors : Anand Upadhyay, Parth Kantelia, Rohan Parmar

Published in: Advances in Information Communication Technology and Computing

Publisher: Springer Singapore

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Abstract

We propose a diabetic retinopathy (DR) analysis algorithm based on probabilistic neural network (PNN). This algorithm is used to recognize the pattern problem. By this algorithm, we can help in diagnosis of a diabetic patient regarding their damage to the back of retina (eye) occurred in tissue of blood vessels using probabilistic neural network. PNN is also known as feed forward neural network. This algorithm has been tested on a small image database and compared with the performance of a human eye. Confusion matrix and kappa coefficient are used to find the accuracy rate of the diabetic eye.

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Literature
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Sopharak A, Uyyanonvara B, Barman S, Williamson TH (2008) Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods. Comput Med Imaging Graph 32(8):720–727CrossRef
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Schaefer G, Leung E (2007) Neural networks for exudate detection in retinal images. In: International symposium on visual computing. Springer, Berlin, Heidelberg, pp 298–306
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Solanki MS (2015) Diabetic retinopathy detection using eye images. Artif Intell Course Project 1–10
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Doshi D, Shenoy A, Sidhpura D, Gharpure P (2016) Diabetic retinopathy detection using deep convolutional neural networks. In: 2016 International conference on computing, analytics and security trends (CAST). IEEE, pp 261–266
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De la Calleja J, Tecuapetla L, Medina MA, Bárcenas E, Nájera ABU (2014) LBP and machine learning for diabetic retinopathy detection. In: International conference on intelligent data engineering and automated learning. Springer, Cham, pp 110–117
Metadata
Title
Retinopathy Detection Using Probabilistic Neural Network
Authors
Anand Upadhyay
Parth Kantelia
Rohan Parmar
Copyright Year
2021
Publisher
Springer Singapore
Book
Advances in Information Communication Technology and Computing

Print ISBN: 978-981-15-5420-9

Electronic ISBN: 978-981-15-5421-6

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-981-15-5421-6_26